KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection

Timo Hromadka, Timotej Smolen, Tomas Remis, Branislav Pecher, Ivan Srba
4/24/2023
cs.CLcs.LG

No official code found. Best implementation: kinit-sk/semeval2023-task3-persuasion-techniques (6 stars, last commit 2023-05-12).

Abstract

This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.

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Code Implementations(1)

kinit-sk/semeval2023-task3-persuasion-techniquesCommunity100%
60Python, Jupyter NotebookFeb 22, 20233 years ago

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Cite this paper

@article{hromadka2023kinitveraai,
  title  = {KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection},
  author = {Timo Hromadka and Timotej Smolen and Tomas Remis and Branislav Pecher and Ivan Srba},
  year   = {2023},
  eprint = {2304.11924},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.11924v1}
}

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